By: Syed Raiyan Ali – syedraiyanali@gmail.com, Department of computer science and Engineering( Data Science ), Student of computer science and Engineering( Data Science ), Madanapalle Institute Of Technology and Science, 517325, Angallu , Andhra Pradesh.
ABSTRACT
AI powered robo-investment consultants have completely changed the game in terms of wealth management services by making it possible for portfolio management, improving course of action towards footing and advising on what to invest in. Their efficiency is expressed through cost saving as well as being available for everyone and always watching out for your interests that is why advanced investment management becomes more accessible. In spite of that, they are characterized by absence of individualized human connections or reliance on algorithms and past information. Even though robo-advisors are able to provide much needed assistance when it comes to cost effectiveness or openness, such a system cannot match an intricate understanding or compassion that comes with having a real person by your side. Wealth’ s future will be dominated by a hybrid approach that combines strengths from AI driven robo advisors’ arsenal alongside personalized services offered by human experts thus leading to provision of complete and balanced investment schemes.
Keywords: AI driven investment, wealth management, portfolio management, risk assessment, personalized investment strategies, financial technology, hybrid investment model.
INTRODUCTION
A paradigm shift is being experienced by the financial sector through the invention of artificial intelligence (AI)[1]. Efficiency, accuracy and personalization in investment strategies and wealth management have taken a new direction due to AI-driven investments. This paper addresses how AI has changed investment management for good; in particular it highlights robo-advisors’ roles, benefits and limitations associated with incorporating AI into wealth management as well as future directions that such type or class of management may take.
THE ROLE OF AI IN INVESTMENT MANAGEMENT
AI Driven Robo Advisors
Robo-advisors are online platforms that utilize AI algorithms for managing investment portfolios[2]. The systems analyze a large amount of data in order to offer personalized investment advice, optimize asset allocation as well as continuously tracking the performance of portfolios. By using machine learning and big data analytics, robo-advisors can adjust investment strategies in real-time based on both market conditions and personal profiles of individual investors.
Benefits of AI Driven Investment Strategies
- Cost Efficiency: Robo-advisors are an inexpensive substitute for conventional financial advisors[3]. As a result of process automation, there will be less human involvement thereby reducing costs thus allowing a wider range of people to access professional investment management.
- Accessibility: Wealth Management is a real challenge that needs to be tackled though there exist some AI-based platforms which provide investment strategies even to those who have little financial understanding and those with minimal capital. This provides access to a wider range of individuals in the financial markets.
- Continuous Monitoring and Adjustment: Staying on market trends and individual portfolios’ performances through progressive AI systems which allow real-time changes that improve returns while decreasing the peril involved is one of the critical roles they can play. As a result, there will be increases in their ability to withstand adverse situations in the financial markets.
- Personalization: By definition, AI algorithms also passed the notion that the investment strategies to be deployed are always dependent on the investor, depending on such characteristics as their willingness on risk, their investment objectives, and time horizon. Thus, effort is made to ensure that both the strategies to be invested in have to meet the particular needs and wants of each person investing.
LIMITATIONS AND CHALLENGES
Lack of Personalized Human Interaction
Even though robo-advisors have a lot of benefits, they do not possess the individualistic and sympathetic nature of the humans who give financial advice[4]. Current artificial intelligence does not possess human attributes such as empathy when it comes to complex financial issues or emotionally driven decision making. Thus an investor will probably find himself yearning for the comfort and subtle hand-holding that can only be provided by a human being.
Dependence on Algorithms and Historical Data
The primary basis for AI driven investment strategies are algorithms and past data. While patterns can be recognized and predictions made, such models may find it hard to adjust to unusual market situations or black swan events[5]. Additionally, relying on past data introduces biases that can decrease the accuracy of predictions.
Data Privacy and Security Concerns
The privacy of data is at stake when artificial intelligence is used for managing wealth. This is because robo-advisors handle sensitive financial information that can make them vulnerable to hacking attempts. As such, there is an imperative for enacting stringent measures to enhance the trust of investors and be in conformity with the laws specified by the regulations.
THE FUTURE OF WEALTH MANAGEMENT
Integration AI with Human Advisors
Wealth management may adopt a hybrid model in the probably future, which will integrate global heroes arsenals powered by AI robos and personalization extensions provided by human advisors. Hence, this collusion endeavors to provide sophisticated and comprehensive investment solution leveraging on efficacy of AI technology infused with personal advice and care bestowed upon by professionals.
Case Studies and Empirical Evidence
Several financial institutions have started to incorporate artificial intelligence in their wealth management services. Case studies and empirical evidence show that hybrid models enhance investment performance and client satisfaction[6]. The examples cited earlier show how AI can be used to supplement human understanding, thereby making for more educated choices in terms of investments as well as better returns financially.
Emerging Trends and Technologies
Wealth management is being affected in the upcoming days by the fast shifting world of AI technology[7]. On the other hand, improving AI’s ability to understand subjective data and consumer sentiment is one of the new trends in NLP and sentiment analysis. Therefore, thanks to changes in quantum processing, we can expect better and accurate forecasting models in finance.
CONCLUSION
The AI-driven investment methods and wealth management have significantly changed the finance sector. Robo-advisors offer many benefits, including low cost, round-the-clock accessibility, monitoring in real-time and personalized financial plans. However, the limitations imposed on AI due to lack of a personalized human interaction and reliance on historical data require a combination of AI and human advisors in a mixed form of advice. This makes it possible to create all-inclusive and neutral strategies for investing that draw upon both human strengths and those of machines. As technology continually opens up new frontiers, wealth management is likely going to follow suit opening up new challenges and opportunities for individual investors as well as banks.
REFERENCES
- D. K. Nguyen, G. Sermpinis, and C. Stasinakis, “Big data, artificial intelligence and machine learning: A transformative symbiosis in favour of financial technology,” Eur. Financ. Manag., vol. 29, no. 2, pp. 517–548, 2023, doi: 10.1111/eufm.12365.
- M. Shanmuganathan, “Behavioural finance in an era of artificial intelligence: Longitudinal case study of robo-advisors in investment decisions,” J. Behav. Exp. Finance, vol. 27, p. 100297, Sep. 2020, doi: 10.1016/j.jbef.2020.100297.
- L. Brenner and T. Meyll, “Robo-advisors: A substitute for human financial advice?,” J. Behav. Exp. Finance, vol. 25, p. 100275, Mar. 2020, doi: 10.1016/j.jbef.2020.100275.
- I. Nain and R. Sruthi, “A Scoping Review on the Factors Affecting the Adoption of Robo-advisors for Financial Decision-Making,” Sci. Pap. Univ. Pardubice Ser. Fac. Econ. Adm., vol. 32, Mar. 2024, doi: 10.46585/sp32011884.
- L. Triyono, – Prayitno, M. Rahaman, – Sukamto, and A. Yobioktabera, “Smartphone-based Indoor Navigation for Guidance in Finding Location Buildings Using Measured WiFi-RSSI,” JOIV Int. J. Inform. Vis., vol. 6, no. 4, pp. 829–834, Dec. 2022, doi: 10.30630/joiv.6.4.1528.
- C.-Y. Lin, M. Rahaman, M. Moslehpour, S. Chattopadhyay, and V. Arya, “Web Semantic-Based MOOP Algorithm for Facilitating Allocation Problems in the Supply Chain Domain,” Int. J. Semantic Web Inf. Syst., vol. 19, pp. 1–23, Jan. 2023, doi: 10.4018/IJSWIS.330250.
- B. Libai et al., “Brave New World? On AI and the Management of Customer Relationships,” J. Interact. Mark., vol. 51, no. C, pp. 44–56, 2020.
- Sharma, K., & Gupta, B. B. (2016). Multi-layer defense against malware attacks on smartphone wi-fi access channel. Procedia Computer Science, 78, 19-25.
- Gupta, B. B., & Quamara, M. (2020). Decentralised control-based interaction framework for secure data transmission in internet of automated vehicles. International Journal of Embedded Systems, 12(4), 414-423.
Cite As
Ali S.R. (2024) PREDICTIVE ANALYTICS IN BANKING: THE ROLE OF AI, Insights2Techinfo, pp.1